Emotion analysis, an integral aspect of human–machine interactions, has witnessed
significant advancements in recent years. With the rise of multimodal data sources such as …

The popularity of wearable devices that record the photoplethysmographic (PPG) signal is
increasing their use as monitors of circulatory and nervous systems function, including the …

Photoplethysmography (PPG) signal comprises physiological information related to
cardiorespiratory health. However, these signals are often contaminated by motion artifacts …

Heart rate variability (HRV) is a vital sign with the potential to predict stress and various
diseases, including heart attack and arrhythmia. Typically, hospitals utilize …

This paper proposes a machine learning approach to detect threats using short-term PPG
(photoplethysmogram) signals from a commercial smartwatch. In supervised learning …

mHealth services use multi-modal machine learning (MMML) models to process
physiological and contextual data for automated decision making. Run-time input data …

Heart rate and respiratory rate are critical vital signs in infants, used as non-invasive
indicators to assess health status and address potential issues. However, many healthcare …

Heart Rate Variability (HRV) can provide extensive information about human health.
Calculating HRV requires careful measurement (in milliseconds) of the time interval …

Better patient outcomes and prompt care depend on early detection of heart attacks. In this
current work, we use the infamous MIT-BIH Arrhythmia Dataset, a reference resource for …

Wrist-worn photoplethysmography (PPG) has become a popular method for continuous and
remote heart rate monitoring, but single-wavelength PPG faces limitations in accuracy …